MEC-enabled 5G Use Cases: A Survey on Security Vulnerabilities and Countermeasures

The future of mobile and internet technologies are manifesting advancements beyond the existing scope of science. The concepts of automated driving, augmented-reality, and machine-type-communication are quite sophisticated and require an elevation of the current mobile infrastructure for launching. The fifth-generation (5G) mobile technology serves as the solution, though it lacks a proximate networking infrastructure to satisfy the service guarantees. Multi-access Edge Computing (MEC) envisages such an edge computing platform. In this survey, we are revealing security vulnerabilities of key 5G-based use cases deployed in the MEC context. Probable security flows of each case are specified, while countermeasures are proposed for mitigating them.

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Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning

Telecom ◽

10.3390/telecom2040026 ◽

2021 ◽

Vol 2 (4) ◽

pp. 446-471

Author(s):

Percy Kapadia ◽

Boon-Chong Seet

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Access Network ◽

Edge Computing ◽

Fifth Generation ◽

Radio Access ◽

Ping Pong ◽

Lstm Network ◽

Multi Access

This paper proposes a potential enhancement of handover for the next-generation multi-tier cellular network, utilizing two fifth-generation (5G) enabling technologies: multi-access edge computing (MEC) and machine learning (ML). MEC and ML techniques are the primary enablers for enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC). The subset of ML chosen for this research is deep learning (DL), as it is adept at learning long-term dependencies. A variant of artificial neural networks called a long short-term memory (LSTM) network is used in conjunction with a look-up table (LUT) as part of the proposed solution. Subsequently, edge computing virtualization methods are utilized to reduce handover latency and increase the overall throughput of the network. A realistic simulation of the proposed solution in a multi-tier 5G radio access network (RAN) showed a 40–60% improvement in overall throughput. Although the proposed scheme may increase the number of handovers, it is effective in reducing the handover failure (HOF) and ping-pong rates by 30% and 86%, respectively, compared to the current 3GPP scheme.

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5G Virtualized Multi-access Edge Computing Platform for IoT Applications

Journal of Network and Computer Applications ◽

10.1016/j.jnca.2018.05.001 ◽

2018 ◽

Vol 115 ◽

pp. 94-102 ◽

Cited By ~ 21

Author(s):

Han-Chuan Hsieh ◽

Jiann-Liang Chen ◽

Abderrahim Benslimane

Keyword(s):

Edge Computing ◽

Computing Platform ◽

Iot Applications ◽

Multi Access

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Programmability of Connectivity Control

Elektronika ir Elektrotechnika ◽

10.5755/j02.eie.28819 ◽

2021 ◽

Vol 27 (2) ◽

pp. 78-85

Author(s):

Ivaylo I. Atanasov ◽

Evelina N. Pencheva

Keyword(s):

Access Network ◽

Edge Computing ◽

Core Network ◽

Access Technology ◽

Radio Access Network ◽

Radio Access Technology ◽

Fifth Generation ◽

Radio Access ◽

Multi Access ◽

Network Interfaces

Network programmability and edge computing as key features of next generation communications enable innovative services. While the programmability is focused on the core network of the fifth-generation system, the edge computing moves the network intelligence to the radio access network. This paper presents a study on the programmability of connectivity control as a function of radio access network using Multi-access Edge Computing. The capability of using more than one radio access technology simultaneously enhances reliability and increases the throughput, especially in dense networks. Opening the radio access network interfaces for programmability of multi-connectivity enables analytics applications to control the device connections to multiple radio links simultaneously based on information of radio conditions, user location or specific policies. The research novelty is in opening the radio access network interfaces for edge applications to access connectivity control.

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Design of an On-Demand Agile 5G Multi-Access Edge Computing Platform Using Aerial Vehicles

IEEE Communications Standards Magazine ◽

10.1109/mcomstd.001.2000016 ◽

2020 ◽

Vol 4 (4) ◽

pp. 34-41

Author(s):

Fabrizio Granelli ◽

Cristina Costa ◽

Jiajing Zhang ◽

Riccardo Bassoli ◽

Frank H.P. Fitzek

Keyword(s):

Edge Computing ◽

Computing Platform ◽

On Demand ◽

Aerial Vehicles ◽

Multi Access

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Enhancing Sensing and Decision-Making of Automated Driving Systems With Multi-Access Edge Computing and Machine Learning

IEEE Intelligent Transportation Systems Magazine ◽

10.1109/mits.2019.2953513 ◽

2020 ◽

pp. 0-0

Author(s):

Allan M. de Souza ◽

Horacio F. Oliveira ◽

Zhongliang Zhao ◽

Torsten Braun ◽

Leandro Villas ◽

...

Keyword(s):

Machine Learning ◽

Decision Making ◽

Edge Computing ◽

Automated Driving ◽

Multi Access

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THE MULTI-ACCESS EDGE COMPUTING APPLICATIONS AND DEVELOPMENT ANALYSIS FOR TELECOMMUNICATION NETWORKS

Telecom IT ◽

10.31854/2307-1303-2020-8-1-28-33 ◽

2020 ◽

Vol 8 (1) ◽

pp. 28-33 ◽

Cited By ~ 1

Author(s):

D. Kashkarov ◽

A. Koucheryavy

Keyword(s):

System Analysis ◽

Educational Process ◽

Edge Computing ◽

Telecommunication Networks ◽

Practical Relevance ◽

Research Subject ◽

Fifth Generation ◽

Scientific Organizations ◽

Multi Access ◽

Development Analysis

Research subject. The article is devoted to the analysis of the applications and development the multi-access edge computing on fifth-generation and beyond telecommunication networks. Method. System analysis. Core results. The identification of development perspectives of a multi-access edge computing on fifth-generation and beyond telecommunication networks. Practical relevance. The results of the article can be used by scientific organizations when planning the development of telecommunication networks, as well as universities in the educational process.

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